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Reinforcement Learning (RL)

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Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/PeterJochem/Deep_RL

Continuous control with deep reinforcement learning

https://github.com/PeterJochem/Double_Deep_QLearning

Continuous control with deep reinforcement learning

https://github.com/PettingZoo-Team/PettingZoo

PettingZoo: Gym for Multi-Agent Reinforcement Learning

https://github.com/PhamHuygaco/my_repo

Obstacle Avoidance and Navigation Utilizing Reinforcement Learning with Reward Shaping

https://github.com/Philori22/DDPG-aigym

Continuous control with deep reinforcement learning

https://github.com/Phutoast/Learning2RL

Learning to reinforcement learn

https://github.com/PierreMarza/autonerf

Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation

https://github.com/Pieter-Cawood/Reinforcement-Learning

The NetHack Learning Environment

https://github.com/PlaytikaOSS/pybandits

Optimal and Adaptive Off-policy Evaluation in Contextual Bandits

https://github.com/PriyankaDatar/NLP_Project_Modfications

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

https://github.com/PriyankaDatar/NLP_SQL_Bert

SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

https://github.com/PriyankaDatar/NLP_SQL_Project

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning

https://github.com/PriyankaDatar/NLP_project_BIDAF

SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning

https://github.com/PwnerHarry/META

A Greedy Approach to Adapting the Trace Parameter for Temporal Difference Learning

https://github.com/PwnerHarry/MTA

A Greedy Approach to Adapting the Trace Parameter for Temporal Difference Learning

https://github.com/PyTorchRL/pytorchrl

Integrating Distributed Architectures in Highly Modular RL Libraries

https://github.com/Qtsho/model-based-rl-navigation

Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning

https://github.com/QueensGambit/CrazyAra

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/QueensGambit/CrazyAra-Engine

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/QuentinVacher-rl/SoftActorCritic-in-Cpp-using-LibTorch

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/R-Stefano/DQN

Playing Atari with Deep Reinforcement Learning

https://github.com/RENHANFEI/patch_sup

Playing FPS Games with Deep Reinforcement Learning

https://github.com/RENHANFEI/vizdoom_health_gathering

Playing FPS Games with Deep Reinforcement Learning

https://github.com/RENHANFEI/vizdoom_patch_dqn

Playing FPS Games with Deep Reinforcement Learning

https://github.com/RENHANFEI/vizdoom_patch_supreme

Playing FPS Games with Deep Reinforcement Learning

https://github.com/RESQUELAB/RL-Teacher-UIAdaptation

Deep reinforcement learning from human preferences

https://github.com/RL-Research-Cohiba/Reinforcement_Learning

Goal-directed graph construction using reinforcement learning

https://github.com/RL-WFU/multi_agent_attack

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/RLAgent/state-marginal-matching

Unifying Count-Based Exploration and Intrinsic Motivation

https://github.com/RLeike/connect-four

Playing Atari with Deep Reinforcement Learning

https://github.com/RUCAIBox/R1-Searcher

R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning

https://github.com/Rabrg/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/Rafael1s/Deep-Reinforcement-Learning-Udacity

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/Rajesh-Siraskar/RL_VC_CodeOcean

Reinforcement Learning for Control of Valves

https://github.com/RamiSketcher/decision-transformer-ammi

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/RandyDeng/gym_connect4

Playing Atari with Deep Reinforcement Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.